Anime Recommendation Engine using Machine Learning

Sakura Suggest

This project is an Anime Recommendation Engine built using machine learning techniques, specifically utilizing cosine similarity for recommendations. It provides personalized anime recommendations based on user preferences and past interactions.

Features

• Cosine Similarity: Measures the similarity between users' preferences and anime features.
• Firebase Integration: Uses Firebase as the backend database to store user data and interactions.
• Web Interface: Interactive web interface for users to get recommendations.

Installation

Prerequisites

• Python 3.6 or higher
• Flask
• pandas
• scikit-learn
• numpy
• Firebase Admin SDK

Steps

1. Navigate to the project directory:

   cd Anime-recommendation-Engine-using-ML

2. Install the required packages:

   pip install -r requirements.txt

3. Set up Firebase:
   • Follow the instructions on the Firebase Console to create a project and obtain the serviceAccountKey.json file.
   • Place the serviceAccountKey.json file in the project directory.

Usage

1. Run the application:

   python app.py

2. Open your browser and go to http://127.0.0.1:5000/ to access the web interface.

Project Structure

• app.py: Main application script.
• anime.csv: Dataset containing information about various anime.
• data.csv: Dataset containing user interaction data.
• serviceAccountKey.json: Firebase service account key.
• templates/: HTML templates for the web interface.
• static/: Static files (CSS, JavaScript) for the web interface.

Machine Requirements

• Python 3.6 or higher
• A modern code editor like Visual Studio Code
• Web browser (Chrome, Firefox, etc.)

live site: https://anime-recommendation-engine-using-ml-khmb.onrender.com/